@inproceedings{FHPBRDDJ03,
  author = {Richard J.~Freuler and Michael J.~Hoffmann and Theodore P.~Pavlic and James M.~Beams and Jeffrey P.~Radigan and Prabal K.~Dutta and John T.~Demel and Erik D.~Justen},
  title = {Experiences with a Comprehensive Freshman Hands-on Course--Designing, Building, and Testing Small Autonomous Robots},
  booktitle = {Proceedings of the 2003 American Society for Engineering Education Annual Conference \& Exposition},
  year = {2003},
  abstract = {During the past ten years, The Ohio State University's College of Engineering has been aggressively addressing the issue of student retention. A major element in this effort is the development of a first-year engineering program that has moved from a series of related but separate courses for first-year engineering fundamentals to a framework that involves two course sequences with tightly coupled courses. Engineering orientation, engineering graphics, and engineering problem solving with computer programming are now offered in each of two course sequences, one called the Fundamentals of Engineering and the other the Fundamentals of Engineering for Honors. These course sequences retain part of the traditional material but now projects. Teamwork, project roles in both with a design/build project course in the Fundamentals of Engineering for Honors sequence that serves as a academic year.}
}

@mastersthesis{Pavlic07,
  author = {Theodore P.~Pavlic},
  title = {Optimal Foraging Theory Revisited},
  school = {The Ohio State University},
  year = {2007},
  address = {Columbus, OH},
  abstract = {Optimal foraging theory explains adaptation via natural selection through quantitative models. Behaviors that are most likely to be favored by natural selection can be predicted by maximizing functions representing Darwinian fitness. Optimization has natural applications in engineering, and so this approach can also be used to design behaviors of engineered agents. In this thesis, we generalize ideas from optimal foraging theory to allow for its easy application to engineering design. By extending standard models and suggesting new value functions of interest, we enhance the analytical efficacy of optimal foraging theory and suggest possible optimality reasons for previously unexplained behaviors observed in nature. Finally, we develop a procedure for maximizing a class of optimization functions relevant to our general model. As designing strategies to maximize returns in a stochastic environment is effectively an optimal portfolio problem, our methods are influenced by results from modern and post-modern portfolio theory. We suggest that optimal foraging theory could benefit by injecting updated concepts from these economic areas.},
  pages = {122},
  url = {http://www.ohiolink.edu/etd/view.cgi?acc_num=osu1181936683}
}

@article{PavlicPassino09,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Foraging theory for autonomous vehicle speed choice},
  journal = {Engineering Applications of Artificial Intelligence},
  year = {2009},
  volume = {22},
  pages = {482--489},
  number = {3},
  month = {April},
  abstract = {We consider the optimal control design of an abstract autonomous vehicle (AAV). The AAV searches an area for tasks that are detected with a probability that depends on vehicle speed, and each detected task can be processed or ignored. Both searching and processing are costly, but processing also returns rewards that quantify designer preferences. We generalize results from the analysis of animal foraging behavior to model the AAV. Then, using a performance metric common in behavioral ecology, we explicitly find the optimal speed and task processing choice policy for a version of the AAV problem. Finally, in simulation, we show how parameter estimation can be used to determine the optimal controller online when density of task types is unknown.},
  doi = {10.1016/j.engappai.2008.10.017},
  issn = {0952-1976},
  keywords = {Intelligent control, optimal control, task-type choice, speed-accuracy trade-off, speed-cost trade-off, decision-making algorithms},
  url = {http://www.sciencedirect.com/science/article/B6V2M-4VBC5KR-2/2/fc603922585631f6d6e1b5efa76551ef}
}

@inproceedings{PavlicPassino09_ICAM2009_CTP_poster,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Cooperative Task Processing},
  booktitle = {Proceedings of the {ICAM} 2009 Symposium: Emergence in Physical, Biological, and Social Systems {IV}},
  year = {2009},
  note = {Poster abstract},
  address = {Ann Arbor, Michigan},
  month = {November 13,}
}

@phdthesis{Pavlic10,
  author = {Theodore P.~Pavlic},
  title = {Design and Analysis of Optimal Task-Processing Agents},
  school = {The Ohio State University},
  year = {2010},
  address = {Columbus, OH},
  month = {August}
}

@inproceedings{PavlicPassino09_SocBio_DACTP,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Design and Analysis of Cooperative Task Processing Agents},
  booktitle = {Proceedings of the Third Annual Frontiers in Life Sciences conference~-- Social Biomimicry: Insect Societies and Human Design},
  year = {2010},
  address = {Tempe, Arizona},
  month = {February 18--20,},
  note = {Poster abstract}
}